Show me where you can quantitatively determine when a novel trait has been produced. Unfortunately the equations you have told me about, the Hardy-Weinburg equations only deal with predictions about alleles. Not new traits. That one is not hardy enough.

An excerpt by Daniel O’Neil who is a professor of the
Behavioral Sciences Department, Palomar College, San Marcos, California admits there’s a problem:

Despite the fact that evolution is a common occurrence in natural populations, allele frequencies will remain unaltered indefinitely unless evolutionary mechanisms such as mutation and natural selection cause them to change.
http://anthro.palomar.edu/synthetic/synth_2.htm

In other words, evolution sure is happening, but not here.

Morphology deals with examining similar physical traits and features. I wish to see this branch of science do a quantitative analysis showing how traits change over time to produce new traits, and then verify those results by testing them against reality. So far no go though.

See if you can answer this for me:
How do you quantitatively determine a new trait has come into existence by changing over time? In other words: When does a new trait get determined after X amount of time has passed? How do you quantify a characteristic?

The answer is not under any of the coconut shells on the table of science.

Where you said:

But most concepts in biology can be applied to modeling. Fitness, mutation rate, morphological change – all those things are modeled daily.

Modeling is great. You can make your model do whatever you want on the computer. It’s funny how you have to invoke an intelligent cause in order to make artificial organisms on computers come into existence. A mind and machine have to physically place the algorithm in the form of code (machine code) onto another machine (computer) to make a simulated organism, proving that an intelligent cause must be present to produce the effect. Evolutionary modeling is pretty much proving intelligent design. An intelligent cause is followed by action (programming) in order to actualize (Intelligent Design) an artificial organism at the beginning stages of life’s supposed history, or whatever (because you can program it to do whatever).

The main point of the whole article was about showing that measurements do not have to be represented as exact quantifiable amounts. What I was trying to reinforce, is, that terms like, ‘fitness’ and ‘adaptation’ are used by evolutionists to determine and explain the existence of novel traits, which are arbitrary terms and are not absolutely quantifiable in terms of measuring change. This means that CSI should not have to be determined true, if and only if it is able to determine the exact quantity measurement of information or complexity in the structure that is in question.

As BarryA said about CSI, “it is present or it is not present”. This means CSI can be represented more as a boolean expression based on specific criteria (the steps in the EF).

Well stated, and I agree, but this of course assumes an isolation of these islands that is denied by the Darwinists, who always claim there actually are countless “islets” of function in a constantly changing configuration space that allows a long series of relatively short jumps to reach the highly functionally specified organization containing total complexity beyond the Dembski bound. In other words, supposedly there is always a chain of islets where each one slightly increases its CSI, ending up with the final CSI beyond the Dembski bound.

So it comes back to demonstrating that this profusion of “islets” of function doesn’t really exist. This is really an alternate statement of Behe’s irreducible complexity argument.

Excellent, I am really excited. Our wishes and desires have taken wings!! Could you please provide us links to the published models for how the human eye evolved? And the human mind, with its varied capabilities? And blood clotting? And the built in GPS units found in various birds and fish? And the echo location found in bats? And why humans love lots of chili peppers and jalepenos, not to mention chocolate?

Wow, I must have slept through the biggest scientific breakthroughs since the theories of relativity and the discovery of DNA. Did anyone else miss it as well, or am I along in this?

You’re joking, right?

There are scads of equations dealing with measurements of fitness (not exact, mind you, but useful models). Probably the most famous of these would be the Hardy-Weinberg equations.

When comparing two fossils, morphological characteristics are compared quantitatively. I.e., is the brain case sufficiently different in volume to a point where we might suspect this is a separate species? Does this correlate with other changes in morphology (e.g., femur size, pelvic tilt, whatever) to bolster this hypothesis.

And if you want really quantitative stuff for change over time, then look at mutations. Synonymous, nonsynonymous, indels, etc.

Nothing in biology is a clear line – this individual has this quantitative fitness. Or this line separates species X from species Y. But most concepts in biology can be applied to modeling. Fitness, mutation rate, morphological change – all those things are modeled daily.

How do you quantify fitness with an exact measurement and test it against reality as it relates to survival?

How is co-option quantified with exact measurements that can lead to predictability?

Who has quantified relatedness and its determination?

When someone sees two fossils in the ground, what quantitative analysis is done to show change over time? How does this quantitative analysis get tested against reality to show change? Has this been applied to body plans, tissue types, organs, cell types, and the machinery within the cell? Does this lead to predictions that can be determined to happen in the future?

Are there exact measurements of quantity associated with change over time?

Does a forensic detective need to know mathematical models and statistical analysis to detect intelligent agency at a crime scene?

Equally — and as pointed out above — ALL measurements incorporate a subjective element.

Perhaps so, but should that be a reason to not undertake the effort, as Barry seems to be suggesting?

In short you may be falling into dismissive, selective hyperskepticism; which is inevitably incoherent.

I am just asking a question so I can understand better. You shouldn’t be so dismissive as me just because I don’t everything there is about ID.

So what are we waiting for? Why not establish a pilot program by identifying a small number of biological functions, organs, and/or organisms. Then we design and develop the most efficient models possible, and we have a quantity in terms of bits.

Of course critics will claim that what is most efficient is subjective. Excellent, they and everyone else are welcome to design and develop their own simulation models. Why not offer awards for the most efficient? For example, trophies of Charles Darwin with his famous hat and beard, along with a totally puzzled and confused expression on his face.

Now of course the simulation models will utilize processes found in nature, e.g., random number generators. Great, these “calls” will be subtracted out in order to arrive at a more true CSI measure.

Critics want predictions, do they? Fantastic, once several simulation models are built we will become very good at predicting CSI measures for additional target processes.

The simulation models will provide an additional benefit. Each point or “node” in the model can be analyzed as to the probability that it was derived by natural means. It will be loads of fun to then multiply the probabilities together, the numbers will be astronomical beyond all plausibility, what a hoot it will be!! We will then establish a lottery with the same odds, and publicly challenge our Materialist friends to play the lottery with their own personal funds. Maybe we can embarass and impoverish them all in one sweet and grand gesture!!!

Oh, of course there is one wrinkle in this entire proposal. And that is that we have no idea how some of the greatest functions in biology work. The human mind for example. Hmmm. Well, we can say one thing for sure, the CSI elevator ain’t anyway near the top floor, if there is a top floor.

Going up????

There are many metrics of information, and some of them have different uses.

In situations where sequence of choice is important the metric you discuss may be important. [There are such things as sequential, memory embedding systems, and combinational, sequence-independent ones. Feedbacks with lags are one way to get such effects,a nd systems where state changes and inrternal state affects response to next input will be sequential -- check up finite state machine algebra. Oddly, a combination lock is sequential, and an ordinary key-lock is combinational in this sense!]

GEM of TKI

It From Bit Excerpt:

But Zeilinger and Brukner noticed that it (Shannon Information) doesn’t take into account the order in which different choices or measurements are made.

This is fine for a classical hand of cards. But in quantum mechanics, information is created in each measurement–and the amount depends on what is measured when–so the order in which different choices or measurements are made does matter, and Shannon’s formula doesn’t hold. Zeilinger and Brukner have devised an alternative measure that they call total information, which includes the effects of measurement. For an entangled pair, the total information content in the system always comes to two bits.

So my question is, “When will Zeilinger’s definition of total information come into play when quantifying CSI as opposed to how information is “normally” defined?”